In modern automotive applications, a vehicle may be equipped with one or more systems such as radar systems, which can provide data to the vehicle, pertaining to the environment of the vehicle. Thus the data may be provided from radar detections (reflection data), and used to provide e.g. a 2 dimensional map of objects or the environment in the vicinity of the vehicle. Typically the radar returns give data on the distance to an object of the environment, for a particular planar angle/segment.
Such systems are often required, or part of, modern Advance Driver Assisted Systems (ADAS) where there is a need to describe or characterize the static environment of the host vehicle. Once the environment of the vehicle has been determined or characterized, this enables the vehicle system to determine, e.g. driving options, areas of the environment which are prohibited for motion of the vehicle (i.e., which allows for example determination of obstacles) and to provide information on available parking spaces. So to summarize, in such systems the environment is characterized or defined on the basis of input data from various sensors. In the case of radar based systems, the input data may consist of so called (point) detections, i.e. spatial locations of obstacles, which are detected from reflected waves of sufficient amplitude from a radar system.
The problem of static environment description in ADAS systems is relatively new. Various methodologies are known to provide for the description of the (e.g. dynamic) environment e.g. moving cars, and the use of occupancy grid map methods are known. Such methodologies have been used for the basis of more complex approaches for the description of the vehicle surroundings. As mentioned such systems are used e.g. for automated parking. The definition of the environment relies on determining the shapes (contours) of boundaries of the prohibited areas/obstacles. Theoretically, the simplest way to provide contour data is a direct connecting of consecutive point detections (sorted by azimuth angle) to form a contour comprising a polyline. So such a polyline comprises straight lines joining point detections. The polyline thus is a 2-D representation of obstacles/environment in the horizontal ground plane.
However, taking the factors described above into account, this approach is very inaccurate. In a single radar scan some gaps can occur in the areas that contain flat surfaces. On the other hand, in the areas containing for example plants, bushes, or geometrically complex static structures, the number of detections can be locally increased. Due to limited capacity of automotive communication interfaces like CAN or Flexray, using all detections available in a single scan, is usually impossible. All this causes that there is a high demand for accurate and simultaneously efficient, in terms of computational complexity, algorithms in this area.
It is one object of the invention thus to approximate shapes of static objects in the environment of a vehicle by providing contours thereof, by generation of by polylines (based on sensor detections), which accurately describe shapes and obstacles with respect to the environment using as few points as possible, in a limited time.